Digital industrial control systems (ICS) have proliferated over the last few decades. With the growth of ICS has come massive growth in data collection and storage of ICS. For example, one combined-cycle power plant can generate four terabytes of data in a single year while a steam-turbine power plan can generate 18 TBs or more of date each year. Much of this data is alarm data that is associated with alarm and alert monitoring and interaction capabilities for various types of devices, such as sensors, pumps, valves, and the like that are controlled and/or monitored by the ICS. Because of the massive increase in alarm data, alarm rationalization has become important.
Alarm rationalization is the process of reviewing, validating, and justifying alarms that meet the criteria of an alarm. In other words, the rationalization specifies only those points in the process system that require alarming. Presently, much of the alarm rationalization process is performed manually—engineers review alarm database and make decisions regarding the alarm rationalization process. These decisions are then hard-coded into the software that comprises the ICS. Furthermore, the process is not discrete—it needs to be repeated over the life of the ICS as components are added or removed from the process and the ICS is adapted accordingly. Because of the huge amounts of data that must be analyzed and the time and effort required, there is a desire to use data analytics and machine learning techniques in order to support the process of alarm rationalization for industrial control systems
Therefore, systems and methods are desired that overcome challenges in the art, some of which are described above.